Machine Learning Algorithms Free Course

Machine Learning Algorithms

star 4.49  Beginner level 2.25 learning hrs 32.5K+ Learners

Enroll in this Machine Learning Algorithms course to understand the machine learning methods, algorithms, and techniques employed to analyze and present data for decision-making. Gain a finer hold through demonstrated projects.

Instructor:

Mr. Anirudh Rao

Key Highlights

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About this course

This online Machine Learning Algorithms course has been designed keeping in mind that a novice learner should be able to grasp the concepts and understand algorithms with examples. This course covers the introduction to Machine Learning and the basics of algorithms, along with a theoretical and practical understanding of supervised, unsupervised, and reinforcement learning. You will also gain skills to employ K-nearest Neighbor, Naive Bayes and Random Forest algorithms, and Linear Regression and Support Vector Machines (SVM) techniques to accomplish Machine Learning tasks. A tonne of practical Python demonstrations is offered to comprehend the concepts better. 

 

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Course outline

Introduction to Machine Learning

This section defines Machine Learning and explains it with an example. 

Types Of Machine Learning

This section discusses Supervised and Unsupervised Machine Learning methods to accomplish various tasks. 

How does a Machine Learning Model Learn?

This section explains how a machine understands to work on a dataset to deliver desired results. It explains the role of pre-fed data set and the process involved in building a Machine Learning model. 
 

Linear Regression Algorithm

This section explains the Linear Regression algorithm with demonstrated example. 

Naïve Bayes Algorithm

This section explains the Naive Bayes algorithm with demonstrated examples. 

KNN Algorithm in Machine Learning

This section explains the KNN algorithm with demonstrated examples. 

Support Vector Machines in Machine Learning

This section explains Support Vector Machine with demonstration example and discusses its applications. 

Random Forest Algorithm in Machine Learning

This section explains the Random Forest algorithm with demonstrated example.

Get access to the complete curriculum once you enroll in the course

Machine Learning Algorithms

rating icon 4.49

2.25 Hours

Beginner

32.5K+ learners enrolled so far

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Machine Learning Essentials with Python
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Learner reviews of the Free Courses

4.49
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Reviewer Profile

5.0

India
“My Experience with Machine Learning Algorithms”
I particularly enjoyed learning about various machine learning algorithms, such as K-means and KNN, and their applications. The clear explanations of how these algorithms work and how to implement them in real-world scenarios made the concepts much more accessible. I also appreciated the hands-on experience with data, which helped solidify my understanding of algorithm performance and selection criteria. Overall, the practical exercises and detailed discussions were very beneficial.
Reviewer Profile

5.0

India
“Well-Structured Curriculum Covering Foundational and Advanced Topics”
The emphasis on practical implementation through projects and assignments has been incredibly valuable. It allowed me to apply theoretical knowledge to real-world problems, which reinforced my understanding of ML algorithms. The interactive teaching approach, including live sessions and Q&A opportunities, was excellent. The instructors were knowledgeable and responsive, addressing doubts effectively.
Reviewer Profile

5.0

“Clear Explanations of Machine Learning Algorithms”
The hands-on coding exercises are well-structured and cover essential aspects of implementing the algorithms. Consider adding more diverse datasets for practice and including challenges that require tuning hyperparameters or optimizing algorithms.
Reviewer Profile

5.0

India
“Comprehensive Course on Machine Learning”
The course offered a deep dive into the fundamental concepts and advanced techniques used in machine learning, empowering me to understand how algorithms learn from data. Key Highlights: Diverse Curriculum: I explored a range of topics, from supervised and unsupervised learning to advanced techniques like neural networks and natural language processing. This breadth of knowledge has provided me with a well-rounded foundation in machine learning.
Reviewer Profile

5.0

India
“Great Learnings and In-Depth Insights About ML Algorithms”
The course offers comprehensive insights into machine learning algorithms, covering essential concepts and practical applications. It effectively balances theory with hands-on exercises, enabling learners to grasp complex topics with ease. The engaging content and supportive community foster an enriching learning experience, making it a valuable resource for both beginners and experienced practitioners.
Reviewer Profile

4.0

India
“Supervised and Unsupervised Learning”
The "Machine Learning Algorithms" course offers a comprehensive introduction to key concepts and techniques in machine learning. It covers supervised and unsupervised learning, including algorithms like KNN, logistic regression, and clustering methods. The curriculum combines theoretical knowledge with practical applications, enhancing understanding through quizzes and hands-on projects. Engaging content and a structured approach make it ideal for both beginners and those looking to deepen their expertise in the field.
Reviewer Profile

5.0

India
“Transformative Learning Journey in Machine Learning”
I appreciated the hands-on approach and practical projects that reinforced theoretical concepts. The curriculum was well-structured, covering a wide range of algorithms and real-world applications. The support from instructors and access to resources made learning engaging and effective. I especially enjoyed collaborating with peers on challenging assignments, which enhanced my understanding and boosted my confidence in applying machine learning techniques.
Reviewer Profile

5.0

India
“Transformative Learning Journey Through Machine Learning”
I enjoyed the hands-on projects that allowed me to apply theoretical concepts in real-world scenarios. The collaborative environment fostered discussion and diverse perspectives, enriching my understanding. The feedback from peers and instructors was invaluable, helping me to identify strengths and areas for improvement. Overall, the experience enhanced my critical thinking skills and deepened my passion for the subject matter.
Reviewer Profile

5.0

India
“Comprehensive Understanding of Machine Learning Concepts”
The course on Great Learning covered a range of machine learning algorithms, starting with foundational concepts like data preprocessing and feature selection, which are essential for effective model building. The course then explored supervised learning algorithms such as linear regression, decision trees, and support vector machines, providing insights into when and how to apply each. Unsupervised learning techniques, including clustering and dimensionality reduction, were also covered, showcasing how they help uncover hidden patterns in data.
Reviewer Profile

4.0

India
“Deep Understanding of Core Concepts”
My journey through this course has been truly transformative. I gained a comprehensive understanding of the core concepts, which significantly enhanced my knowledge base. The emphasis on practical applications allowed me to see how theoretical principles can be implemented in real-world scenarios. Additionally, I developed essential skills such as critical thinking and problem-solving, which I can apply in my future endeavors. The collaborative learning environment fostered engaging discussions with peers, enriching my experience and providing diverse perspectives.

Our course instructor

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Mr. Anirudh Rao

Machine Learning Expert

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784K+ Learners
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79 Courses
Anirudh has been working in the field of Data Science and has expertise over Python, Machine Learning and other concepts in the field of data analysis. He is also proficient in the concept of Deep Learning and its usage in a production environment. Expertise extends towards working on various projects in the domain of Artificial Intelligence and Neural Networks as well.

Frequently Asked Questions

Will I receive a certificate upon completing this free course?

Yes, upon successful completion of the course and payment of the certificate fee, you will receive a completion certificate that you can add to your resume.

Is this course free?

Yes, you may enroll in the course and access the course content for free. However, if you wish to obtain a certificate upon completion, a non-refundable fee is applicable.

What are the prerequisites required to learn Machine Learning Algorithms?

Basic computer literacy, Math would be an added advantage; some basic understanding of how to code in Python can ​speed up learning Machine Learning Algorithms. 

 

How long does it take to complete learning basic algorithms for Machine Learning?

It takes about 1 and a half hours to complete the course. 

 

What are Machine Learning Algorithms?

With Machine Learning algorithms, software programs can predict outcomes more accurately without having to be explicitly instructed. They use these algorithms to forecast new output values by feeding historical data.

 

Why is Machine Learning important?

Machine Learning is significant because it uses various algorithms to help companies build new goods by providing insights into consumer behavior trends and operational business patterns. Machine learning is a key component of the operations of many of the world's most successful businesses today, like Facebook, Google, and Uber. For numerous businesses, machine learning has significantly increased their competitive edge.

 

Why is Machine Learning popular?

Machine learning is one of the most important technologies today. Since it is used in practically every field, it is widely used by professionals, academics, and students. You probably already know how effective and potent a well-trained machine-learning model is in solving issues. This is possible since the algorithms are fed with data, and the result is a model. Since this is a fundamental idea, everyone in the class must fully grasp the algorithms.

 

How to choose a suitable Machine Learning model?

If not done carefully, selecting the best machine learning model to address a problem can take a lot of time. The basic guide to choosing a suitable model:
Step 1: Align the issue with potential data sources that should be considered for the solution. Data scientists and skilled professionals with in-depth knowledge of the issue are needed for assistance with this phase.
Step 2: Gather information, format it, and, if necessary, label it. With assistance from data wranglers, data scientists often take the lead in this step.
Step 3: Select the algorithm(s) to employ, then test them to see how they perform. Data scientists typically handle this stage.
Step 4: Once outputs are accurate enough, they can be further fine-tuned. Data scientists often complete this step with input from subject matter experts who thoroughly understand the issue.
Will I get a certificate after completing this course?
Answer: Yes, you will get a course completion certificate after qualifying in the quiz. 
 

What knowledge and skills will I gain upon algorithms for Machine Learning course?

By the end of this course, you will understand the basics of Machine Learning and fundamental algorithms that can be used in Machine Learning, like Linear Regression, Naive Bayes, KNN, Random Forest algorithms, and Support Vector Machines.

 

Can I take the Machine Learning course multiple times?

Yes. You will have free lifetime access to this course, so you can access the course at your leisure. 

How much does this Machine Learning Algorithms course cost?

It is an entirely free course from Great Learning Academy. Anyone interested in learning the basics of Machine Learning Algorithms can get started with this course.

Can I sign up for multiple courses from Great Learning Academy at the same time?

Yes, you can enroll in as many courses as you want from Great Learning Academy. There is no limit to the number of courses you can enroll in at once, but since the courses offered by Great Learning Academy are free, we suggest you learn one by one to get the best out of the subject.

Why choose Great Learning Academy for this free Machine Learning Algorithms course?

Great Learning Academy provides this Machine Learning Algorithms course for free online. The course is self-paced and helps you understand various topics that fall under the subject with solved problems and demonstrated examples. The course is carefully designed, keeping in mind to cater to both beginners and professionals, and is delivered by subject experts. Great Learning is a global ed-tech platform dedicated to developing competent professionals. Great Learning Academy is an initiative by Great Learning that offers in-demand free online courses to help people advance in their jobs. More than 5 million learners from 140 countries have benefited from Great Learning Academy's free online courses with certificates. It is a one-stop place for all of a learner's goals.

What are the steps to enroll in this Machine Learning Algorithms course?

Enrolling in any of the Great Learning Academy’s courses is just one step process. Sign-up for the course, you are interested in learning through your E-mail ID and start learning them for free online.

Will I have lifetime access to this free Machine Learning Algorithms course?

Yes, once you enroll in the course, you will have lifetime access, where you can log in and learn whenever you want to. 

Is there any limit on how many times I can take this free course?

Once you enroll in the Machine Learning Algorithms course, you have lifetime access to it. So, you can log in anytime and learn it for free online.

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